After successfully training your ML model and after selecting the best run, you are about to deploy it as a web service to the production environment.
Because you anticipate a massive amount of requests to be handled by the service, you choose AKS as a compute target.
You are using the following script to deploy your model:
# deploy model inference_config = InferenceConfig(runtime= "python", entry_script=script_file, conda_file=env_file) deployment_config = AciWebservice.deploy_configuration(cpu_cores = 1, memory_gb = 4) service_name = "fraud-detection-service" service = Model.deploy(ws, service_name, [model], inference_config, deployment_config) service.wait_for_deployment(True) print(service.state)Running the deployment script results in the service state “Failed”.You have a look at your scoring script and you suspect that something is wrong with getting data in the run() function:
# scoring script ... def init(): global model # Get the path to the deployed model file and load it model_path = Model.get_model_path('fraud_detection_model') model = joblib.load(model_path) # Called when a request is received def run(raw_data): # Get the input data as a numpy array data = np.array(json.loads(raw_data)['data']) # Get a prediction from the model predictions = model.predict(data) # Get the classnames for predictions classnames = ['non-fraud', 'fraud'] predicted_classes = [] for prediction in predictions: predicted_classes.append(classnames[prediction]) # Return the predictions as JSON return json.dumps(predicted_classes) Is this the best way to localize the error?
Click on the arrows to vote for the correct answer
A. B.Answer: B.
Option A is incorrect because you experienced the error while deploying the service, which means that the deployment process failed.
It typically occurs when the get_model_path() function which is called during the deployment while initializing the model cannot locate the model's path for some reason.
Therefore, you should look at the init() function first, for example this line:
<pre class="brush:java;"> model_path = Model.get_model_path('fraud_detection_model')</pre>Option B is CORRECT because the error occurred during the deployment of the service, which means that it is not a data-related problem (if it was, it would occur during inference phase, while running the service)
Since the deployment has failed, the problem is not inference-related.
You'd better check the init() function.
Reference:
No, this is not the best way to localize the error.
The error message provided by the deployment script is not sufficient to determine the exact cause of the failure. It only indicates that the service failed to deploy, but does not provide any information about the specific error.
The scoring script provided in the question may contain errors that could cause the deployment to fail. However, it is not certain that the scoring script is the cause of the failure, as there could be other factors at play such as configuration issues, networking problems, or resource constraints.
To properly diagnose the error, it is important to investigate the deployment logs and error messages in detail. These logs can provide more specific information about the cause of the failure, such as missing dependencies or incorrect configuration settings.
In addition to examining the logs, it may also be useful to test the scoring script in isolation to ensure that it is functioning correctly. This can be done by running the script locally and passing in sample data to verify that it produces the expected output.
Once the cause of the error has been identified, appropriate measures can be taken to fix the issue and redeploy the service. This may involve modifying the scoring script, updating dependencies, or adjusting the deployment configuration.